ConnGA: Genetic Algorithm-Based Framework for Optimizing DTI Tractography in Pre-Clinical Murine Models of Alzheimer’s Disease

Abstract

We have established methods to acquire 25 µm resolution Diffusion Tensor Imaging (DTI) in postmortem mouse brain models of Alzheimer’s disease (AD). However, the utility of these advances remains limited by suboptimal post-processing pipelines. Here, we develop a genetic algorithm (GA) to identify tractography parameters required to enhance disease-specific disruptions in brain structural connectivity in the BXD-77 murine model. Given the susceptibility of tractography to false positives, we evaluate the generalizability of GA-optimized tracking across multiple BXD strains (BXD-101, BXD-65, BXD77, and BXD-32) and assess how variations in tractography parameters influence the relationship between amyloid pathology and structural connectivity. We also quantify the relative importance of individual tractography parameters in determining group separation. GA-optimized tracking produced substantial gains in group separability and showed consistency with phenotypic gradients observed across multiple strains, while demonstrating improved agreement with histological data. This work presents a robust optimization framework that enhances the reliability and generalizability of tractography-based connectomic analyses in preclinical models of neurodegeneration.